Updated: 2020-07-26 16:24:36 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Contained and Uncontained Disease

A key indicator of mitigation is capping infection. Uncontained disease growth threatens epidemic conditions.

This visualization shows places where current disease levels are below their peak levels (’contained“) and where current disease levels are at an all time high (”uncontained").

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from log_2(R_e) > 0 to log_2(R_e) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

state R_e cases daily_cases
Mississippi 1.27 50834 1400
Missouri 1.27 37188 1082
Montana 1.27 3247 143
Wyoming 1.27 2440 54
Kentucky 1.26 27312 703
North Dakota 1.26 5653 131
Oklahoma 1.20 30108 885
Nevada 1.19 42672 1322
Alabama 1.18 78996 2253
Idaho 1.18 18449 670
Tennessee 1.18 89575 2495
Virginia 1.18 67339 894
Arkansas 1.17 36774 843
California 1.17 455415 11271
Indiana 1.17 62909 890
Colorado 1.16 43556 584
Connecticut 1.16 48405 109
New Mexico 1.16 18876 339
West Virginia 1.16 5889 153
Wisconsin 1.16 48235 1023
Georgia 1.15 149737 3729
Florida 1.14 421807 13443
Illinois 1.14 170148 1378
Louisiana 1.14 106038 2450
Maryland 1.14 83166 814
Nebraska 1.14 24163 258
Texas 1.13 401192 11657
Washington 1.13 54386 1008
Kansas 1.12 25754 540
Minnesota 1.12 50048 690
New Hampshire 1.12 6398 29
Ohio 1.11 83581 1546
Oregon 1.11 16652 377
North Carolina 1.10 112090 2204
South Carolina 1.10 81402 2097
Pennsylvania 1.09 111208 949
South Dakota 1.09 8104 62
Utah 1.08 37850 694
Delaware 1.07 14014 108
Iowa 1.07 42167 578
Rhode Island 1.07 16686 80
Maine 1.06 3785 20
Massachusetts 1.06 114893 289
Michigan 1.06 85921 729
New Jersey 1.05 180034 282
Vermont 1.04 1396 8
Arizona 1.02 161211 3072
New York 1.02 415924 737

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. They’re plotted against linear scales. While this hides some important details, the plots are more intuitively interpretable for most people.

## Warning: Removed 1 row(s) containing missing values (geom_path).

Mortality Trend

National Reproduction Rates \(R_e\)

There is also large variation in the distribution of \(R_e\) values. This shows how that distribution has changed over the last three weeks. As a reminder, for disease reduction, \(R_e\) needs to be sustained below 1.0.

Trend

Distribution of \(R_e\) Values

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Okanogan WA 14 1 1.6 612 1470 48
King WA 1 2 1.1 14248 660 193
Pierce WA 4 3 1.2 4785 560 109
Spokane WA 6 4 1.2 3287 660 107
Yakima WA 2 5 1.0 10112 4060 116
Snohomish WA 3 6 1.1 5308 670 65
Kitsap WA 16 7 1.3 484 180 19
Benton WA 5 9 1.1 3345 1720 67
Franklin WA 7 11 1.0 3168 3490 53
Clark WA 8 12 1.0 1689 360 38
Grant WA 9 13 1.1 1131 1190 24
OR
county ST case rank severity R_e cases cases/100k daily cases
Multnomah OR 1 1 1.1 3938 490 88
Umatilla OR 4 2 1.1 1687 2190 56
Marion OR 3 3 1.1 2381 710 44
Washington OR 2 4 1.1 2479 430 51
Deschutes OR 8 5 1.2 440 240 17
Jefferson OR 13 6 1.3 247 1070 9
Clackamas OR 5 7 1.0 1255 310 22
Lane OR 7 10 1.0 453 120 12
Malheur OR 6 14 0.8 593 1950 16
Union OR 9 23 0.7 388 1490 1
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Kern CA 6 1 1.8 14765 1670 1097
Los Angeles CA 1 2 1.1 174061 1720 3279
San Bernardino CA 4 3 1.2 28811 1350 886
Riverside CA 2 4 1.1 34516 1450 738
Fresno CA 7 5 1.2 12548 1280 398
San Joaquin CA 9 6 1.2 10333 1410 344
Orange CA 3 7 1.0 34498 1090 819
San Diego CA 5 8 1.1 26943 820 587
Alameda CA 8 16 1.1 10365 630 207

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 1.0 108197 2540 2225
Pima AZ 2 2 1.0 14845 1460 245
Yuma AZ 3 3 1.0 10153 4890 142
Pinal AZ 4 4 1.0 7431 1770 141
Mohave AZ 8 5 1.1 2741 1330 76
Gila AZ 12 6 1.2 674 1260 22
Graham AZ 14 7 1.2 366 970 16
Navajo AZ 5 8 1.0 5104 4700 57
Apache AZ 6 10 1.1 2891 4040 26
Santa Cruz AZ 9 11 1.1 2485 5330 26
Coconino AZ 7 12 1.0 2860 2040 31
CO
county ST case rank severity R_e cases cases/100k daily cases
Denver CO 1 1 1.2 8986 1300 99
El Paso CO 4 2 1.1 4100 600 90
Arapahoe CO 2 3 1.1 6414 1010 66
Chaffee CO 17 4 1.4 261 1360 16
Adams CO 3 5 1.1 5469 1100 62
Jefferson CO 5 6 1.2 3562 620 48
Larimer CO 9 7 1.1 1239 370 28
Douglas CO 8 8 1.1 1495 450 29
Weld CO 6 9 1.1 3393 1150 30
Boulder CO 7 12 1.1 1765 550 19
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 1.0 18135 1620 281
Utah UT 2 2 1.1 7097 1200 151
Davis UT 3 3 1.1 2705 790 73
Washington UT 5 4 1.1 2144 1340 47
Weber UT 4 5 1.1 2338 940 62
Cache UT 6 6 1.1 1732 1420 10
Tooele UT 10 7 1.0 492 750 12
San Juan UT 8 8 1.1 580 3800 10
Summit UT 7 11 0.9 669 1650 7
Wasatch UT 9 12 0.9 513 1680 5
NM
county ST case rank severity R_e cases cases/100k daily cases
Bernalillo NM 1 1 1.1 4372 650 113
Lea NM 7 2 1.4 503 720 24
Rio Arriba NM 12 3 1.4 284 720 16
Doña Ana NM 4 4 1.1 1986 920 45
Valencia NM 10 5 1.2 337 440 13
McKinley NM 2 6 1.0 3883 5330 20
Otero NM 5 7 1.2 1023 1560 7
Santa Fe NM 8 8 1.1 493 330 13
Sandoval NM 6 10 1.1 997 710 14
Curry NM 9 12 1.1 359 720 9
San Juan NM 3 14 0.9 2926 2300 15

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Middlesex NJ 4 1 1.3 17667 2140 39
Ocean NJ 7 2 1.2 10196 1720 26
Camden NJ 9 3 1.0 8039 1580 24
Bergen NJ 1 4 1.0 20442 2200 24
Gloucester NJ 16 5 1.1 2933 1010 15
Monmouth NJ 8 6 1.0 9913 1590 25
Atlantic NJ 14 7 1.1 3223 1200 15
Essex NJ 3 9 1.0 19490 2460 17
Passaic NJ 5 11 1.0 17412 3450 14
Union NJ 6 13 0.9 16749 3030 8
Hudson NJ 2 15 0.8 19517 2920 13
PA
county ST case rank severity R_e cases cases/100k daily cases
Philadelphia PA 1 1 1.1 29324 1860 154
Allegheny PA 4 2 1.0 7352 600 195
Delaware PA 3 3 1.2 8206 1460 55
Chester PA 9 4 1.1 4593 890 48
Bucks PA 5 5 1.1 6640 1060 47
Franklin PA 18 6 1.3 1167 760 16
Montgomery PA 2 7 1.1 9415 1150 46
Berks PA 7 9 1.2 4977 1190 26
Lancaster PA 6 12 1.0 5262 980 35
Lehigh PA 8 19 1.0 4654 1280 18
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore MD 3 1 1.2 10666 1290 168
Baltimore city MD 4 2 1.1 10180 1660 141
Prince George’s MD 1 3 1.1 21813 2410 133
Anne Arundel MD 5 4 1.1 6311 1110 65
Montgomery MD 2 5 1.0 16919 1630 93
Worcester MD 16 6 1.3 481 930 16
Dorchester MD 19 7 1.4 302 940 10
Harford MD 9 8 1.2 1560 620 26
Howard MD 6 9 1.1 3322 1050 41
Frederick MD 7 10 1.2 2862 1150 23
Charles MD 8 12 1.1 1711 1090 17
VA
county ST case rank severity R_e cases cases/100k daily cases
Virginia Beach city VA 5 1 1.3 3390 750 154
Norfolk city VA 8 2 1.2 2743 1120 115
Newport News city VA 9 3 1.3 1448 800 67
Patrick VA 65 4 1.8 81 450 5
Suffolk city VA 14 5 1.2 893 1000 31
Hampton city VA 15 6 1.2 868 640 34
Fairfax VA 1 7 1.1 15259 1330 64
Chesterfield VA 4 8 1.1 3693 1090 41
Prince William VA 2 9 1.0 8418 1840 48
Henrico VA 6 10 1.1 3224 990 32
Loudoun VA 3 12 1.0 4817 1250 30
Arlington VA 7 23 0.9 2808 1210 14
WV
county ST case rank severity R_e cases cases/100k daily cases
Kanawha WV 2 1 1.3 658 350 24
Mingo WV 19 2 1.5 91 370 7
Ohio WV 7 3 1.2 227 530 8
Hancock WV 20 4 1.3 87 290 5
Monongalia WV 1 5 0.9 889 840 27
Raleigh WV 14 6 1.2 130 170 5
Cabell WV 5 7 1.1 266 280 6
Wayne WV 9 9 1.1 179 440 5
Berkeley WV 3 10 1.0 607 530 6
Jefferson WV 4 17 0.8 282 500 2
Wood WV 6 18 0.6 228 270 3
Randolph WV 8 19 0.6 208 720 1
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1.1 6414 1160 53
Sussex DE 2 2 1.1 5495 2500 33
Kent DE 3 3 1.1 2105 1200 21

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Baldwin AL 8 1 1.4 2662 1280 144
Jefferson AL 1 2 1.1 10399 1580 312
Mobile AL 2 3 1.2 7140 1720 201
Madison AL 4 4 1.1 4428 1240 183
Houston AL 16 5 1.4 1096 1050 48
Calhoun AL 19 6 1.3 1064 920 53
Shelby AL 6 7 1.2 2767 1310 93
Montgomery AL 3 8 1.1 5675 2500 95
Tuscaloosa AL 5 16 1.1 3539 1720 69
Marshall AL 7 17 1.1 2684 2820 56
Lee AL 9 18 1.1 2379 1490 58
MS
county ST case rank severity R_e cases cases/100k daily cases
Jackson MS 6 1 1.6 1514 1070 84
Coahoma MS 35 2 1.6 527 2210 35
Hinds MS 1 3 1.3 4339 1790 130
Alcorn MS 59 4 1.6 244 660 18
Rankin MS 4 5 1.3 1762 1170 61
Winston MS 39 6 1.5 474 2580 20
Calhoun MS 51 7 1.5 340 2330 20
Washington MS 9 8 1.3 1261 2680 51
DeSoto MS 2 9 1.2 2796 1590 76
Harrison MS 5 11 1.2 1739 860 55
Madison MS 3 13 1.2 1994 1930 48
Forrest MS 8 20 1.2 1363 1800 31
Jones MS 7 23 1.2 1510 2210 26
LA
county ST case rank severity R_e cases cases/100k daily cases
Allen LA 26 1 1.5 927 3610 53
Calcasieu LA 6 2 1.1 5344 2670 189
East Baton Rouge LA 3 3 1.1 9559 2150 212
Vernon LA 39 4 1.4 560 1100 31
Acadia LA 15 5 1.2 2196 3510 82
Jefferson LA 1 6 1.0 13294 3050 177
Evangeline LA 32 7 1.4 631 1880 32
Lafayette LA 4 9 1.0 5767 2400 158
Tangipahoa LA 9 12 1.1 2757 2110 76
Caddo LA 5 13 1.1 5544 2230 102
St. Tammany LA 7 15 1.1 4213 1670 90
Orleans LA 2 21 1.0 9827 2520 94
Ouachita LA 8 23 1.1 3981 2550 72

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Columbia FL 30 1 1.8 2186 3160 206
Miami-Dade FL 1 2 1.2 103243 3800 3521
Broward FL 2 3 1.2 49244 2580 1762
Wakulla FL 51 4 1.8 484 1520 44
Palm Beach FL 3 5 1.1 30044 2080 791
Orange FL 5 6 1.1 27089 2050 737
Marion FL 23 7 1.3 3361 960 185
Duval FL 6 10 1.1 19812 2140 572
Hillsborough FL 4 11 1.0 27139 1970 630
Polk FL 9 12 1.1 11443 1710 357
Lee FL 8 16 1.0 14779 2060 403
Pinellas FL 7 22 1.0 15275 1590 334
GA
county ST case rank severity R_e cases cases/100k daily cases
Wayne GA 47 1 1.7 552 1850 48
Fulton GA 1 2 1.2 15638 1530 444
Cobb GA 4 3 1.2 9886 1330 264
Gwinnett GA 2 4 1.1 15181 1680 319
Chatham GA 6 5 1.2 4227 1470 152
DeKalb GA 3 6 1.1 11018 1480 229
Richmond GA 9 7 1.2 2746 1360 97
Clayton GA 7 8 1.1 3947 1420 98
Hall GA 5 11 1.1 4876 2490 90
Muscogee GA 8 23 1.0 3855 1960 85

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Bexar TX 3 1 1.3 37781 1960 1635
Hidalgo TX 6 2 1.3 15940 1880 768
Harris TX 1 3 1.1 64908 1410 1630
Cameron TX 10 4 1.3 8005 1900 407
DeWitt TX 76 5 1.7 506 2480 44
Calhoun TX 87 6 1.7 365 1670 28
Dallas TX 2 7 1.0 46976 1820 1061
Tarrant TX 4 8 1.1 25277 1250 616
Nueces TX 8 18 1.1 10735 2980 361
El Paso TX 7 20 1.0 13267 1580 318
Travis TX 5 23 0.9 19840 1650 354
Galveston TX 9 30 1.0 8242 2520 178
OK
county ST case rank severity R_e cases cases/100k daily cases
Jackson OK 13 1 1.8 351 1380 37
Oklahoma OK 1 2 1.1 7539 960 236
Tulsa OK 2 3 1.1 7319 1140 188
Cleveland OK 3 4 1.2 2030 730 62
Sequoyah OK 35 5 1.5 130 310 8
Rogers OK 9 6 1.2 562 620 23
Garfield OK 26 7 1.3 230 370 12
Canadian OK 5 9 1.1 813 590 27
McCurtain OK 6 21 1.1 762 2310 12
Payne OK 8 24 1.1 581 710 9
Comanche OK 7 29 1.0 650 530 11
Texas OK 4 43 0.7 1014 4800 1

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Wayne MI 1 1 1.0 25971 1470 146
Macomb MI 3 2 1.1 9036 1040 79
Oakland MI 2 3 1.0 13881 1110 90
Jackson MI 7 4 1.4 2261 1420 11
Genesee MI 5 5 1.1 3266 800 30
Kent MI 4 6 1.0 6715 1040 67
Saginaw MI 8 7 1.1 1701 880 25
Washtenaw MI 6 8 1.0 2746 750 26
Ottawa MI 9 10 1.0 1615 570 25
WI
county ST case rank severity R_e cases cases/100k daily cases
Milwaukee WI 1 1 1.1 17924 1880 345
Waukesha WI 5 2 1.3 2839 710 114
Racine WI 4 3 1.2 2868 1470 48
Kenosha WI 6 4 1.2 2237 1330 44
Marinette WI 27 5 1.4 233 570 14
Brown WI 3 6 1.1 3741 1440 42
Washington WI 12 7 1.2 686 510 18
Outagamie WI 9 9 1.1 931 500 24
Walworth WI 8 10 1.1 1038 1010 23
Dane WI 2 13 0.9 3861 730 62
Rock WI 7 14 1.1 1414 870 22

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Hennepin MN 1 1 1.1 15972 1290 218
Ramsey MN 2 2 1.1 6125 1130 78
Anoka MN 4 3 1.2 2928 840 46
Beltrami MN 32 4 1.5 146 320 11
Dakota MN 3 5 1.1 3381 810 59
Washington MN 7 6 1.1 1634 650 31
Sherburne MN 17 7 1.2 498 530 13
Scott MN 9 8 1.1 1146 800 23
Olmsted MN 8 11 1.0 1509 990 18
Stearns MN 5 17 0.9 2721 1740 14
Nobles MN 6 34 0.9 1724 7890 3
SD
county ST case rank severity R_e cases cases/100k daily cases
Minnehaha SD 1 1 1.1 3960 2120 18
Lincoln SD 4 2 1.2 452 820 6
Pennington SD 2 3 1.0 786 720 12
Davison SD 15 4 1.0 80 400 2
Beadle SD 3 5 0.9 580 3160 2
Union SD 6 6 0.8 174 1150 2
Brown SD 5 7 0.8 375 970 1
Codington SD 7 9 0.7 112 400 1
Buffalo SD 9 11 0.6 109 5310 1
Brookings SD 8 12 0.6 111 320 1
ND
county ST case rank severity R_e cases cases/100k daily cases
Williams ND 4 1 1.4 215 630 16
Burleigh ND 2 2 1.3 722 770 28
Ward ND 6 3 1.4 139 200 7
Morton ND 5 4 1.3 212 690 8
Cass ND 1 5 1.0 2782 1600 26
Grand Forks ND 3 6 1.2 583 830 13
Stutsman ND 10 7 1.3 91 430 3
Stark ND 7 8 1.0 139 450 4
Walsh ND 9 9 1.0 98 910 4
Mountrail ND 8 10 0.9 107 1050 3

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Fairfield CT 1 1 1.3 17329 1840 38
Hartford CT 3 2 1.2 12275 1370 33
New Haven CT 2 3 1.0 12896 1500 25
Litchfield CT 4 4 1.2 1548 850 4
Tolland CT 7 5 1.1 959 630 3
New London CT 6 6 0.8 1368 510 3
Windham CT 8 7 0.7 661 570 2
Middlesex CT 5 8 0.7 1369 840 2
MA
county ST case rank severity R_e cases cases/100k daily cases
Middlesex MA 1 1 1.1 25243 1580 61
Norfolk MA 5 2 1.1 9924 1420 38
Worcester MA 4 3 1.1 13032 1580 32
Suffolk MA 2 4 1.0 20826 2630 41
Essex MA 3 5 1.0 16922 2170 35
Bristol MA 7 6 1.0 8849 1580 29
Barnstable MA 9 7 1.2 1678 790 9
Hampden MA 8 8 1.0 7251 1550 20
Plymouth MA 6 9 1.1 8961 1750 13
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.1 14079 2220 66
Kent RI 2 2 1.0 1362 830 8
Washington RI 3 3 0.8 578 460 2
Bristol RI 5 4 0.8 295 600 2
Newport RI 4 5 0.6 372 450 2

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
New York City NY 1 1 1.0 228181 2700 352
Suffolk NY 3 2 1.0 42826 2880 59
Nassau NY 2 3 1.0 42909 3160 46
Erie NY 7 4 1.0 8276 900 41
Westchester NY 4 5 1.0 35734 3690 34
Albany NY 11 6 1.1 2429 790 18
Monroe NY 8 7 1.0 4582 620 30
Orange NY 6 12 1.0 11010 2910 12
Dutchess NY 9 14 0.9 4421 1500 9
Rockland NY 5 15 0.9 13843 4280 9

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Chittenden VT 1 1 0.9 713 440 5
Bennington VT 5 2 0.6 82 230 1
Rutland VT 4 3 0.5 83 140 0
Franklin VT 2 4 0.5 113 230 0
Windham VT 3 5 0.2 102 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
Cumberland ME 1 1 0.9 2006 690 10
Androscoggin ME 3 2 1.1 525 490 2
York ME 2 3 0.9 614 300 3
Kennebec ME 4 4 1.0 155 130 1
Penobscot ME 5 5 0.7 140 90 1
NH
county ST case rank severity R_e cases cases/100k daily cases
Hillsborough NH 1 1 1.1 3635 880 19
Rockingham NH 2 2 0.9 1579 520 4
Merrimack NH 3 3 0.8 450 300 2
Carroll NH 8 4 0.8 80 170 1
Strafford NH 4 5 0.8 314 240 1
Belknap NH 5 6 0.6 102 170 1
Grafton NH 6 7 0.5 101 110 0
Cheshire NH 7 8 0.3 82 110 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 4 1 1.1 7072 1730 186
Florence SC 11 2 1.3 2415 1740 75
Lexington SC 5 3 1.2 4183 1460 109
Hampton SC 43 4 1.5 245 1240 16
Greenville SC 2 5 1.0 9419 1890 186
Charleston SC 1 6 1.0 10782 2730 239
Sumter SC 12 7 1.2 2056 1920 60
York SC 9 8 1.2 2825 1090 84
Berkeley SC 6 11 1.1 3518 1680 108
Beaufort SC 8 12 1.1 2928 1600 83
Horry SC 3 16 0.9 7520 2340 131
Spartanburg SC 7 17 1.0 3497 1160 83
NC
county ST case rank severity R_e cases cases/100k daily cases
Mecklenburg NC 1 1 1.0 19187 1820 338
Wake NC 2 2 1.1 10004 960 210
Pitt NC 22 3 1.3 1410 790 45
Guilford NC 4 4 1.1 4675 890 94
Cumberland NC 9 5 1.2 2265 680 61
Buncombe NC 23 6 1.2 1404 550 51
Onslow NC 40 7 1.3 699 360 28
Gaston NC 6 10 1.1 2674 1230 75
Forsyth NC 5 12 1.1 4553 1230 70
Durham NC 3 13 1.0 5504 1800 75
Union NC 8 16 1.1 2486 1100 55
Johnston NC 7 25 1.0 2627 1370 44

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Big Horn MT 4 1 1.6 200 1500 11
Flathead MT 5 2 1.5 180 180 12
Gallatin MT 2 3 1.2 781 750 35
Yellowstone MT 1 4 1.1 887 560 35
Cascade MT 8 5 1.4 104 130 6
Lake MT 6 6 1.1 146 490 9
Missoula MT 3 7 1.2 205 180 7
Lewis and Clark MT 7 8 1.2 106 160 6
WY
county ST case rank severity R_e cases cases/100k daily cases
Teton WY 3 1 1.4 271 1180 12
Lincoln WY 9 2 1.4 82 430 5
Sweetwater WY 5 3 1.2 218 490 7
Laramie WY 2 4 1.0 416 430 7
Albany WY 10 5 1.1 81 210 3
Fremont WY 1 6 1.0 451 1130 5
Park WY 8 7 1.0 98 340 2
Uinta WY 4 8 0.9 234 1140 2
Natrona WY 6 9 0.8 191 240 2
Campbell WY 7 10 0.8 106 220 2
ID
county ST case rank severity R_e cases cases/100k daily cases
Canyon ID 2 1 1.2 4167 1960 194
Ada ID 1 2 1.1 7204 1620 256
Bonneville ID 6 3 1.5 487 430 31
Kootenai ID 3 4 1.1 1331 870 55
Jefferson ID 21 5 1.4 85 300 6
Bannock ID 10 6 1.3 300 350 13
Owyhee ID 12 7 1.2 192 1680 10
Minidoka ID 9 8 1.2 364 1770 9
Twin Falls ID 4 9 1.0 1029 1230 16
Cassia ID 7 10 1.1 410 1740 7
Jerome ID 8 18 0.8 381 1630 5
Blaine ID 5 21 0.6 564 2560 1

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Hancock OH 44 1 1.8 193 250 14
Franklin OH 1 2 1.1 15679 1230 306
Lucas OH 4 3 1.2 3989 920 92
Cuyahoga OH 2 4 1.0 11691 930 203
Henry OH 70 5 1.6 95 350 8
Seneca OH 63 6 1.6 104 190 8
Montgomery OH 5 7 1.1 3453 650 82
Hamilton OH 3 9 1.0 8515 1050 123
Summit OH 6 13 1.1 2858 530 43
Butler OH 8 16 1.1 2380 630 45
Marion OH 7 41 1.1 2818 4310 6
Pickaway OH 9 48 1.0 2310 4020 7
IL
county ST case rank severity R_e cases cases/100k daily cases
Cook IL 1 1 1.1 101746 1950 519
Saline IL 51 2 1.8 86 350 9
St. Clair IL 7 3 1.2 3448 1310 83
DuPage IL 3 4 1.1 10762 1160 92
Madison IL 9 5 1.2 1753 660 50
Lake IL 2 6 1.1 11316 1610 80
Will IL 5 7 1.1 8029 1170 62
Kane IL 4 10 1.1 8713 1640 47
McHenry IL 8 14 1.1 2692 870 32
Winnebago IL 6 16 1.1 3473 1210 24
IN
county ST case rank severity R_e cases cases/100k daily cases
Marion IN 1 1 1.2 13599 1440 125
Vanderburgh IN 10 2 1.2 1435 790 63
Hamilton IN 6 3 1.2 2185 690 41
Lake IN 2 4 1.1 6656 1370 82
Dubois IN 28 5 1.3 529 1250 21
St. Joseph IN 5 6 1.1 2770 1030 52
Monroe IN 24 7 1.3 577 400 22
Elkhart IN 3 8 1.0 4405 2160 57
Allen IN 4 15 1.1 3332 900 30
Johnson IN 9 22 1.1 1556 1030 16
Hendricks IN 8 25 1.1 1647 1020 13
Cass IN 7 27 1.2 1702 4470 5

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Henderson TN 39 1 1.8 298 1070 28
Knox TN 5 2 1.4 3077 670 142
Blount TN 20 3 1.5 775 600 49
Shelby TN 2 4 1.1 18570 1980 392
Davidson TN 1 5 1.0 19764 2890 428
Washington TN 24 6 1.5 623 490 40
Carter TN 42 7 1.6 288 510 22
Rutherford TN 3 10 1.2 5279 1720 145
Hamilton TN 4 12 1.1 4914 1370 124
Sumner TN 7 17 1.1 2885 1610 79
Williamson TN 6 19 1.1 2960 1350 94
Wilson TN 8 27 1.1 1860 1400 55
Trousdale TN 9 75 0.9 1563 16330 4
KY
county ST case rank severity R_e cases cases/100k daily cases
Harlan KY 35 1 2.0 158 580 18
Oldham KY 9 2 1.7 510 780 42
Jefferson KY 1 3 1.3 5907 770 134
Scott KY 24 4 1.6 228 430 12
Barren KY 23 5 1.5 243 560 13
Fayette KY 2 6 1.1 2764 870 66
Boyle KY 53 7 1.6 89 300 6
Warren KY 3 8 1.2 2174 1720 38
Kenton KY 4 9 1.2 1168 710 26
Boone KY 5 17 1.1 923 710 17
Daviess KY 7 22 1.1 629 630 11
Shelby KY 6 29 1.0 659 1410 8
Muhlenberg KY 8 44 0.9 617 1990 4

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Polk MO 32 1 2.1 180 570 25
St. Louis MO 1 2 1.3 10424 1040 247
Camden MO 36 3 1.7 165 370 11
St. Charles MO 3 4 1.2 3005 770 125
Greene MO 8 5 1.3 1041 360 51
St. Louis city MO 2 6 1.2 3874 1240 85
Jackson MO 4 7 1.2 2625 380 82
Jefferson MO 5 8 1.2 1147 510 40
Jasper MO 6 18 1.1 1069 900 26
Boone MO 7 22 1.0 1049 590 27
Buchanan MO 9 35 1.0 1014 1140 6
AR
county ST case rank severity R_e cases cases/100k daily cases
Independence AR 32 1 1.7 174 470 13
Garland AR 17 2 1.5 632 640 32
Pulaski AR 2 3 1.1 4307 1090 116
Craighead AR 13 4 1.4 925 880 37
Washington AR 1 5 1.1 5623 2460 86
Sebastian AR 5 6 1.1 1384 1090 47
Pope AR 10 7 1.2 1040 1630 35
Benton AR 3 8 1.0 4180 1610 59
Crittenden AR 8 11 1.2 1063 2170 20
Jefferson AR 7 12 1.2 1150 1630 26
Faulkner AR 9 19 1.1 1046 850 23
Lincoln AR 6 35 1.0 1153 8420 7
Hot Spring AR 4 48 0.5 1471 4390 17

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 508.6 seconds to compute.
2020-07-26 16:33:05

version history

Today is 2020-07-26.
67 days ago: Multiple states.
59 days ago: \(R_e\) computation.
56 days ago: created color coding for \(R_e\) plots.
51 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
51 days ago: “persistence” time evolution.
44 days ago: “In control” mapping.
44 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
36 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
31 days ago: Added Per Capita US Map.
29 days ago: Deprecated national map.
25 days ago: added state “Hot 10” analysis.
20 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
18 days ago: added per capita disease and mortaility to state-level analysis. 6 days ago: changed to county boundarieson national map for per capita disease. 1 days ago: corrected factor of two error in death trend data.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.